Purpose-Bound Data Schema

Data artifactGovernance & ComplianceSafety, Security & Governancearc:PurposeBoundDataSchema

A specification listing, for each processing purpose, the minimal personal-data fields permitted to be collected, stored or used for model training, derived from a per-element necessity assessment.

Responsibility. Limits personal data to what each defined purpose strictly requires.

Also known as: Strict field requirements, Minimal data collection schema

Direct neighbourhood (hover for relationship types)

Relationships

constrains control

Design guidance

SHOULD exclude attributes unnecessary for the purpose, such as demographics, photos or proxies like institution names and zip codes, from model inputs and training data.
SHOULD destroy verification documents once verification is recorded rather than archive them.
SHOULD grant sensitive data (e.g., medical questionnaires) only to the roles that need it, encrypted.

Classification

Patterns: Data minimizationPurpose limitation
Quality attributes: Privacy (NIST AI RMF: privacy-enhanced)Fairness (NIST AI RMF: fair, harmful bias managed)
Risks mitigated: Discrimination via demographic or proxy featuresExpanded data footprint requiring protection
Frameworks & regulations: GDPR Art. 5(1)(c)GDPR Art. 25

Sources

Ch9.7: T. Nguyen, "GDPR and Data Protection Regulations," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 9.7. ISBN: 9798244538229.
Ref9.05: "Privacy and Data Protection for AI Systems," unpublished reference note (references/Chapter 9 - Safety, Ethics, and Compliance/05-Privacy-Data-Protection.md), Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam supplementary materials, 2026. unpublished note